Intelligent community electric vehicle charging dynamic pricing method and system

Abstract

The invention discloses an intelligent community electric vehicle charging dynamic pricing method and system, and the method comprises the steps: a community operator collects the data of served electric vehicles in a future scheduling period, and builds a lower-layer response model with the minimum power utilization cost of an electric vehicle owner as a target; an operator considers future photovoltaic output and electric vehicle charging response, arranges an energy storage system, constructs an upper-layer dynamic electricity price design model with minimum equivalent load curve fluctuation, and considers limitation on upper and lower limits and a mean value of the electricity price; and solving the lower-layer response model and the upper-layer dynamic electricity price design model by adopting a genetic algorithm to obtain a dynamic electricity price parameter with the highest fitness, and issuing the dynamic electricity price parameter at the initial moment of a future scheduling period. The system comprises a lower-layer model building module, an upper-layer model building module and a solving module. The problem that in an existing electric vehicle charging behavior regulation and control model, the expected response is inconsistent with the actual charging behavior is solved.

Description

Technical Field

[0001] The present invention belongs to the field of electric power systems, and in particular relates to a method and system for dynamic pricing of electric vehicle charging in a smart community. Background Art

[0002] With the popularity of electric vehicles, grid load management has become more complicated. The charging behavior of electric vehicles has brought new challenges to the stability of the grid and the dispatch of power supply. Especially during peak hours, disorderly centralized charging of electric vehicles may lead to a sharp increase in power demand, which in turn endangers the safe operation of distribution facilities. On the other hand, the photovoltaic output of smart communities is poorly matched with the daily load power consumption characteristics, causing the peak-to-valley difference of the community net load curve to widen further.

[0003] In view of the limitations of the transformation and expansion of existing community power distribution facilities, it is difficult to equip electric vehicle charging piles with dedicated transformers. Smart community operators hope to equip energy storage systems and use price signals to guide electric vehicle owners to achieve orderly charging of electric vehicles and coordinated scheduling of photovoltaic output during periods of low grid load, so as to achieve the purpose of improving the system load curve. However, most existing models fail to fully consider the interest game relationship between electric vehicle owners and operators, and the load management effect is not ideal. At the same time, the reaction of load response to electricity prices is ignored, resulting in actual charging behavior not being consistent with the expected response, and a lack of in-depth understanding and control of electric vehicle charging behavior.

[0004] Therefore, it is necessary to implement a new pricing mechanism for public charging piles in the community, which can guide reasonable charging behavior, balance the grid load and minimize the electricity cost based on the interests of community operators and electric vehicle owners. To this end, in order to ensure that the charging behavior of electric vehicles is consistent with the expected response, a dynamic pricing method for electric vehicle charging in smart communities is proposed. A two-layer dynamic pricing model for community operators is established, which takes into account the charging behavior of electric vehicle owners, takes the minimum fluctuation of the equivalent load curve as the goal, and arranges the energy storage output. The operator guides electric vehicle owners to independently choose the appropriate charging period by publishing the dynamic electricity price parameters of each period in the future scheduling cycle. In this way, car owners can choose to charge in the period with lower electricity prices according to the electricity price, thereby reducing the charging cost. Through the application of the two-layer master-slave game model and dynamic electricity prices, the invention realizes the balance of interests between agents and electric vehicle owners. In addition, in the actual response, both parties can get the maximum return within their respective interests and avoid potential losses caused by unexpected results. Summary of the invention

[0005] The present invention provides a method and system for charging dynamic pricing of electric vehicles in an intelligent community, aiming to solve the problem of inconsistency between expected response and actual charging behavior in the existing electric vehicle charging behavior control model. The existing model ignores the impact of load response on electricity price, and thus fails to consider the interactive behavior of electric vehicles formed by dynamic electricity price from the bottom layer, resulting in the appearance of unexpected response peaks, causing damage to the interests of operators. To this end, the present invention constructs a pricing model for community operators at the upper layer. The community operator considers the photovoltaic output and arranges the energy storage output with the goal of minimizing the fluctuation of the equivalent load curve, and publishes dynamic electricity price parameters for each period in the future scheduling cycle to electric vehicle owners; the lower-level electric vehicle owners are guided by the dynamic electricity price and independently select the charging period to achieve the minimum charging cost. The introduction of dynamic electricity price incorporates the impact of load response on electricity price, ensuring that there is no potential high electricity price to damage their own interests. The relationship between the community operator and the electric vehicle is established through a two-layer master-slave game model. At the equilibrium, the interests of both parties can be maximized in the actual response, and the incentive compatibility state of both parties is achieved, avoiding the inconsistency between the actual electric vehicle response behavior and the expected response, and the appearance of new response peaks and potential high electricity prices that cause damage to the interests of the community operator and the electric vehicle owner.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A method for dynamic pricing of electric vehicle charging in a smart community, comprising: Step 1: The community operator collects data on electric vehicles served in the future scheduling cycle and builds a lower-level response model with the goal of minimizing the electricity cost of electric vehicle owners; Step 2: The operator considers the future photovoltaic output and electric vehicle charging response and arranges the energy storage system, constructs an upper-level dynamic electricity price design model with the smallest fluctuation of the equivalent load curve, and takes into account the restrictions on the upper and lower limits and mean of the electricity price; Step 3: Use genetic algorithm to solve the lower-level response model and the upper-level dynamic electricity price design model, obtain the dynamic electricity price parameters with the highest fitness and publish them at the initial moment of the future scheduling period.

[0007] The present invention is further improved in that, step 1: the cell operator collects data of electric vehicles served in the future scheduling period, including: The electric vehicle’s arrival time, departure time, battery level at arrival, battery level at departure, and maximum charging power of the electric vehicle. (1) Where: EV a Indicates electric vehicle a data; Indicates electric vehicle a Time to reach the charging station; Indicates electric vehiclea Time away from the charging station; Indicates electric vehicle a The battery level when entering the charging station; Indicates electric vehicle a The battery level when leaving the charging station; Indicates electric vehicle a The upper limit of charging power.

[0008] The present invention is further improved in that, step 1: constructing a lower-level response model with the goal of minimizing the electricity cost of electric vehicle owners, as follows: (2) The constraints of the lower model are as follows: (3) Where: t Represents a time index; Indicates a time interval; represents the cost of charging an electric vehicle; Indicates electric vehicle a exist t The charging power at .

[0009] The present invention is further improved in that, step 2: the operator considers the future photovoltaic output and electric vehicle charging response and arranges the energy storage system, including: the community operator predicts the photovoltaic output and basic load in the future scheduling cycle, and calculates the community equivalent load curve , (2) (3) Where: express t Total load at 10000 Hz; Energy storage system t The charge and discharge power at express t PV output at that time; express t The base load at express t All electric vehicle charging power at the time.

[0010] The present invention is further improved in that, step 2: constructing an upper-level dynamic electricity price design model with the smallest fluctuation of the equivalent load curve, and taking into account the restrictions on the upper and lower limits and the mean of the electricity price, wherein the objective function of the upper-level dynamic electricity price design model is, (4) (5) Where:T Indicates the scheduling period; represents the variance of the equivalent load curve; It represents the average value of equivalent load in the dispatching period; The upper-level dynamic electricity price design model has the following dynamic electricity price constraints: (6) Where: express t Charging electricity price at the time; express t Dynamic electricity price sensitivity coefficient at ; express t Dynamic electricity price benchmark coefficient at the time of express t The lower limit of charging electricity price at that time; express t The upper limit of charging electricity price when The upper-level dynamic electricity price design model has the following constraints on the energy storage system: (7) Where: Indicates the maximum output of the energy storage system; express t The energy storage system capacity at that time; Indicates the initial state of the energy storage system; Indicates the maximum storable power of the energy storage system.

[0011] The present invention is further improved in that, step three: using a genetic algorithm to solve the lower layer response model and the upper layer dynamic electricity price design model, obtaining the dynamic electricity price parameter with the highest fitness and publishing it at the initial moment of the future dispatch period, including: Step 1: Set the population size, number of iterations, and population mutation rate; Step 2: Using the dynamic electricity price sensitivity coefficient and benchmark coefficient set by the cell operator as decision variables, randomly generate the initial population m Group; Step 3: Electric vehicle receiving community operator m The group dynamic electricity price is used to solve the charging amount in each period of the scheduling cycle with the lowest charging cost, and the charging amount is returned to the community operator; Step 4: The cell operator calculates the equivalent load curve fluctuation value based on the power consumption returned during the dispatch period, arranges the output of the energy storage equipment, and records the optimal result; Step 5: The cell operator crosses and mutates the dynamic electricity price coefficients to generate a new dynamic electricity price population; Step 6: Repeat steps 3 and 4 until the termination condition is met, and output the dynamic electricity price corresponding to the smallest fluctuation of the equivalent load curve of the cell operator.

[0012] A further improvement of the present invention is that, at the initial moment of the actual scheduling cycle, the cell operator publishes the dynamic electricity price of its final decision.

[0013] A smart community electric vehicle charging dynamic pricing system, comprising: The lower-level model building module collects the data of electric vehicles served in the future scheduling cycle and builds the lower-level response model with the goal of minimizing the electricity cost of electric vehicle owners; The upper model construction module considers the future photovoltaic output and electric vehicle charging response and arranges the energy storage system, constructs the upper dynamic electricity price design model with the smallest fluctuation of the equivalent load curve, and takes into account the restrictions on the upper and lower limits and mean of the electricity price; The solution module uses genetic algorithms to solve the lower-level response model and the upper-level dynamic electricity price design model, obtains the dynamic electricity price parameters with the highest fitness and publishes them at the initial moment of the future scheduling period.

[0014] A further improvement of the present invention is that in the lower model building module, data of electric vehicles served in the future scheduling period are collected, including: The electric vehicle’s arrival time, departure time, battery level at arrival, battery level at departure, and maximum charging power of the electric vehicle. (1) Where: EV a Indicates electric vehicle a data; Indicates electric vehicle a Time to reach the charging station; Indicates electric vehicle a Time away from the charging station; Indicates electric vehicle a The battery level when entering the charging station; Indicates electric vehicle a The battery level when leaving the charging station; Indicates electric vehicle a The upper limit of charging power.

[0015] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for dynamic pricing of electric vehicle charging in a smart community.

[0016] Compared with the prior art, the present invention has at least the following beneficial technical effects: Compared with the existing traditional master-slave game static electricity price design method, the present invention solves the problem of actual load response deviating from the expected response and damaging the interests of the two main bodies: for operators, after the traditional electricity price is released, the intelligent terminal selects low electricity price time periods for charging according to the settings of electric vehicle owners, resulting in unexpected load peaks; for electric vehicle owners, unexpected load peaks have potential high electricity prices, which increase charging costs. Compared with traditional electricity price design, the dynamic electricity price proposed in the present invention takes into account the effect of load response on electricity prices in the load response stage to avoid the occurrence of load peaks. First, the response to the dynamic electricity price is introduced at the lower level to avoid the hidden dangers of potential high electricity prices and ensure that the charging behavior of electric vehicle owners is consistent with the expected response; further, the operator considers the photovoltaic output and the load response behavior of the lower-level electric vehicles, and the designed dynamic electricity price meets the restrictions on retail electricity prices, and arranges the output of the energy storage system to pursue the minimum fluctuation of the equivalent load curve; finally, a genetic algorithm is used to solve the optimal dynamic electricity price. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a flow chart of a method for dynamic pricing of electric vehicle charging in a smart community.

[0018] Figure 2 This is a schematic diagram of basic load and photovoltaic output.

[0019] Figure 3 This is the load curve under disorderly charging mode.

[0020] Figure 4 This is the load curve in the orderly charging mode.

[0021] Figure 5 Charging electric car cost comparison chart.

[0022] Figure 6 This is a structural block diagram of a dynamic pricing system for charging electric vehicles in a smart community according to the present invention. DETAILED DESCRIPTION

[0023] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0024] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0025] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0026] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0027] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0028] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] Example 1 like Figure 1 As shown, the present invention provides a method for dynamic pricing of electric vehicle charging in a smart community, comprising: Step 1: The community operator collects data on electric vehicles served in the future scheduling cycle and builds a lower-level response model with the goal of minimizing the electricity cost of electric vehicle owners; Step 2: The operator considers the future photovoltaic output and electric vehicle charging response and arranges the energy storage system, constructs an upper-level dynamic electricity price design model with the smallest fluctuation of the equivalent load curve, and takes into account the restrictions on the upper and lower limits and mean of the electricity price; Step 3: Use genetic algorithm to solve the lower-level response model and the upper-level dynamic electricity price design model, obtain the dynamic electricity price parameters with the highest fitness and publish them at the initial moment of the future scheduling period.

[0030] In this embodiment, step 1: the cell operator collects data of electric vehicles served in the future scheduling period, including: The electric vehicle’s arrival time, departure time, battery level at arrival, battery level at departure, and maximum charging power of the electric vehicle. (1) Where: EV a Indicates electric vehicle a data; Indicates electric vehicle a Time to reach the charging station; Indicates electric vehicle a Time away from the charging station; Indicates electric vehicle a The battery level when entering the charging station; Indicates electric vehicle a The battery level when leaving the charging station; Indicates electric vehicle a The upper limit of charging power.

[0031] In this embodiment, step 1: constructing a lower-level response model with the goal of minimizing the electricity cost of electric vehicle owners is as follows: (2) The constraints of the lower model are as follows: (3) Where: t Represents a time index; Indicates a time interval; represents the cost of charging an electric vehicle; Indicates electric vehicle a exist t The charging power at .

[0032] In this embodiment, step 2: the operator considers the future photovoltaic output and electric vehicle charging response and arranges the energy storage system, including: the community operator predicts the photovoltaic output and basic load in the future scheduling cycle, and calculates the community equivalent load curve , (2) (3) Where: express t Total load at 10000 Hz; Energy storage system t The charge and discharge power at express t PV output at that time; express t The base load at express t All electric vehicle charging power at the time.

[0033] In this embodiment, step 2: construct an upper-level dynamic electricity price design model with the smallest fluctuation of the equivalent load curve, and take into account the restrictions on the upper and lower limits and the mean of the electricity price, wherein the objective function of the upper-level dynamic electricity price design model is, (4) (5) Where: T Indicates the scheduling period; represents the variance of the equivalent load curve; It represents the average value of equivalent load in the dispatching period; The upper-level dynamic electricity price design model has the following dynamic electricity price constraints: (6) Where: express t Charging electricity price at the time; express t Dynamic electricity price sensitivity coefficient at ; express t Dynamic electricity price benchmark coefficient at the time of express t The lower limit of charging electricity price at that time; express t The upper limit of charging electricity price when The upper-level dynamic electricity price design model has the following constraints on the energy storage system: (7) Where: Indicates the maximum output of the energy storage system; express t The energy storage system capacity at that time; Indicates the initial state of the energy storage system; Indicates the maximum storable power of the energy storage system.

[0034] In this embodiment, step three: using a genetic algorithm to solve the lower layer response model and the upper layer dynamic electricity price design model, obtaining the dynamic electricity price parameters with the highest fitness and publishing them at the initial moment of the future scheduling period, including: Step 1: Set the population size, number of iterations, and population mutation rate; Step 2: Using the dynamic electricity price sensitivity coefficient and benchmark coefficient set by the cell operator as decision variables, randomly generate the initial population m Group; Step 3: Electric vehicle receiving community operator m The group dynamic electricity price is used to solve the charging amount in each period of the scheduling cycle with the lowest charging cost, and the charging amount is returned to the community operator; Step 4: The cell operator calculates the equivalent load curve fluctuation value based on the power consumption returned during the dispatch period, arranges the output of the energy storage equipment, and records the optimal result; Step 5: The cell operator crosses and mutates the dynamic electricity price coefficients to generate a new dynamic electricity price population; Step 6: Repeat steps 3 and 4 until the termination condition is met, and output the dynamic electricity price corresponding to the smallest fluctuation of the equivalent load curve of the cell operator.

[0035] Among them, at the initial moment of the actual scheduling cycle, the cell operator publishes the dynamic electricity price of its final decision.

[0036] Example 2 An example is used to illustrate the implementation process of this method. A smart community has 500 houses, the electric vehicle penetration rate is 40%, and the number of electric vehicles is 200. The photovoltaic output and base load forecast for the future dispatch cycle are as follows: Figure 2 As shown, the battery capacity of the energy storage system is 1400kWh, and the upper limit of the charging and discharging power of the energy storage is 400kW.

[0037] The three scenarios of disorderly charging mode, static electricity price under orderly charging mode and dynamic electricity price are compared. Disorderly charging mode refers to charging immediately after the last trip, orderly charging mode refers to charging at the lowest charging cost of electric vehicles, static electricity price refers to electricity price that is not related to load response, and dynamic electricity price refers to the electricity price proposed by the present invention. Figure 3 The load curve under disordered charging mode is given; Figure 4 The load curves under static electricity price and dynamic electricity price in orderly charging mode are given; Table 1 shows the performance comparison of equivalent load curves as shown in Table 1. The charging cost of electric vehicles under static electricity price and dynamic electricity price is compared, as shown in Table 1. Figure 5 and Table 2. From the results, it can be seen that the proposed two-tier dynamic pricing strategy can not only take into account the interests of community operators and electric vehicle owners, but also avoid unexpected results such as the emergence of new load peaks under static electricity prices, and can effectively guide the orderly charging of electric vehicles and the dispatch of renewable energy output systems to improve the load curve. Table 1 Comparison of equivalent load curve performance under different scenarios

[0038] Table 2 Comparison of total charging costs of electric vehicles under different scenarios

[0039] Example 3 like Figure 6 As shown, the present invention provides a smart community electric vehicle charging dynamic pricing system, comprising: The lower-level model building module collects the data of electric vehicles served in the future scheduling cycle and builds the lower-level response model with the goal of minimizing the electricity cost of electric vehicle owners; The upper model construction module considers the future photovoltaic output and electric vehicle charging response and arranges the energy storage system, constructs the upper dynamic electricity price design model with the smallest fluctuation of the equivalent load curve, and takes into account the restrictions on the upper and lower limits and mean of the electricity price; The solution module uses genetic algorithms to solve the lower-level response model and the upper-level dynamic electricity price design model, obtains the dynamic electricity price parameters with the highest fitness and publishes them at the initial moment of the future scheduling period.

[0040] The present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method for dynamic pricing of electric vehicle charging in a smart community.

[0041] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0042] The present application is described with reference to the flowcharts and / or block diagrams of the methods, systems and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0043] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0044] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0045] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0046] In addition, it should be understood that although this specification is described in accordance with the implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art. The above content is only to illustrate the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for dynamic pricing of electric vehicle charging in a smart community, characterized in that: include: Step 1: The community operator collects data on electric vehicles served in the future scheduling cycle and builds a lower-level response model with the goal of minimizing the electricity cost of electric vehicle owners; Step 2: The operator considers the future photovoltaic output and electric vehicle charging response and arranges the energy storage system, constructs an upper-level dynamic electricity price design model with the smallest fluctuation of the equivalent load curve, and takes into account the restrictions on the upper and lower limits and mean of the electricity price; Step 3: Use genetic algorithm to solve the lower-level response model and the upper-level dynamic electricity price design model, obtain the dynamic electricity price parameters with the highest fitness and publish them at the initial moment of the future scheduling period.

2. A method for dynamic pricing of electric vehicle charging in a smart community according to claim 1, characterized in that: Step 1: The community operator collects data on electric vehicles to be served in the future dispatch cycle, including: The electric vehicle’s arrival time, departure time, battery level at arrival, battery level at departure, and maximum charging power of the electric vehicle. (1) Where: EV a Indicates electric vehicle a data; Indicates electric vehicle a Time to reach the charging station; Indicates electric vehicle a Time away from the charging station; Indicates electric vehicle a The battery level when entering the charging station; Indicates electric vehicle a The battery level when leaving the charging station; Indicates electric vehicle a The upper limit of charging power.

3. A method for dynamic pricing of electric vehicle charging in a smart community according to claim 2, characterized in that: Step 1: Construct the lower-level response model with the goal of minimizing the electricity cost of electric vehicle owners, as follows: (2) The constraints of the lower model are as follows: (3) Where: t Represents a time index; Indicates a time interval; represents the cost of charging an electric vehicle; Indicates electric vehicle a exist t The charging power at .

4. A method for dynamic pricing of electric vehicle charging in a smart community according to claim 1, characterized in that: Step 2: The operator considers the future photovoltaic output and electric vehicle charging response and arranges the energy storage system, including: the community operator predicts the photovoltaic output and basic load in the future scheduling cycle and calculates the community equivalent load curve , (2) (3) Where: express t Total load at time Energy storage system t The charge and discharge power at express t PV output at that time; express t The base load at express t All electric vehicle charging power at the time.

5. A method for dynamic pricing of electric vehicle charging in a smart community according to claim 4, characterized in that: Step 2: Construct an upper-level dynamic electricity price design model with the smallest fluctuation of the equivalent load curve, and take into account the restrictions on the upper and lower limits and mean of the electricity price. The objective function of the upper-level dynamic electricity price design model is: (4) (5) Where: T Indicates the scheduling period; represents the variance of the equivalent load curve; It represents the average value of equivalent load in the dispatching period; The upper-level dynamic electricity price design model has the following dynamic electricity price constraints: (6) Where: express t Charging electricity price at the time; express t Dynamic electricity price sensitivity coefficient at ; express t Dynamic electricity price benchmark coefficient at the time of express t The lower limit of charging electricity price at that time; express t The upper limit of charging electricity price when The upper-level dynamic electricity price design model has the following constraints on the energy storage system: (7) Where: Indicates the maximum output of the energy storage system; express t The energy storage system capacity at that time; Indicates the initial state of the energy storage system; Indicates the maximum storable power of the energy storage system.

6. A method for dynamic pricing of electric vehicle charging in a smart community according to claim 1, characterized in that: Step 3: Use genetic algorithm to solve the lower-level response model and the upper-level dynamic electricity price design model, obtain the dynamic electricity price parameters with the highest fitness and publish them at the initial moment of the future dispatch period, including: Step 1: Set the population size, number of iterations, and population mutation rate; Step 2: Using the dynamic electricity price sensitivity coefficient and benchmark coefficient set by the cell operator as decision variables, randomly generate the initial population m Group; Step 3: Electric vehicle receiving community operator m The group dynamic electricity price is used to solve the charging amount in each period of the scheduling cycle with the lowest charging cost, and the charging amount is returned to the community operator; Step 4: The cell operator calculates the equivalent load curve fluctuation value based on the power consumption returned during the dispatch period, arranges the output of the energy storage equipment, and records the optimal result; Step 5: The cell operator crosses and mutates the dynamic electricity price coefficients to generate a new dynamic electricity price population; Step 6: Repeat steps 3 and 4 until the termination condition is met, and output the dynamic electricity price corresponding to the smallest fluctuation of the equivalent load curve of the cell operator.

7. A method for dynamic pricing of electric vehicle charging in a smart community according to claim 6, characterized in that: At the beginning of the actual scheduling cycle, the cell operator publishes the dynamic electricity price of its final decision.

8. A dynamic pricing system for electric vehicle charging in a smart community, characterized in that: include: The lower-level model building module collects the data of electric vehicles served in the future scheduling cycle and builds the lower-level response model with the goal of minimizing the electricity cost of electric vehicle owners; The upper model construction module considers the future photovoltaic output and electric vehicle charging response and arranges the energy storage system, constructs the upper dynamic electricity price design model with the smallest fluctuation of the equivalent load curve, and takes into account the restrictions on the upper and lower limits and mean of the electricity price; The solution module uses genetic algorithms to solve the lower-level response model and the upper-level dynamic electricity price design model, obtains the dynamic electricity price parameters with the highest fitness and publishes them at the initial moment of the future scheduling period.

9. The intelligent community electric vehicle charging dynamic pricing system according to claim 8 is characterized in that: In the lower model building module, data on electric vehicles served in the future dispatch cycle are collected, including: The electric vehicle’s arrival time, departure time, battery level at arrival, battery level at departure, and maximum charging power of the electric vehicle. (1) Where: EV a Indicates electric vehicle a data; Indicates electric vehicle a Time to reach the charging station; Indicates electric vehicle a Time away from the charging station; Indicates electric vehicle a The battery level when entering the charging station; Indicates electric vehicle a The battery level when leaving the charging station; Indicates electric vehicle a The upper limit of charging power.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a method for dynamic pricing of electric vehicle charging in a smart community according to any one of claims 1 to 7 are implemented.

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